Guide to Intelligent Document Processing

Research firm IDC predicts that by 2025, the repository of data worldwide will exceed 175 zettabytes. Most of this data will be locked in unstructured data formats like images, emails, and PDFs.

Imagine if you could take all of the unstructured data on your company’s network – from emails and reports to scanned documents and images – and turn it into structured data that can be easily analyzed or acted upon. This is the power of Intelligent Document Processing (IDP). Converting unstructured data into structured data for further analysis or action using IDP gives businesses a competitive edge.

Clearly, IDP is a rapidly growing field with numerous applications in both the public and private sectors.

Understanding Intelligent Document Processing

IDP uses Artificial Intelligence (AI) and Machine Learning (ML) to eliminate the amount of manual processing (and the associated human error factor) required to perform repetitive tasks and extract data from complex documents. 

AI and ML algorithms are increasingly used for Intelligent Document Processing (IDP). IDP involves using computer vision, natural language processing, and other techniques to automatically extract data from digital documents such as images, PDFs, and scans.

This data can then be used for various purposes such as document classification, contract analysis, and automated invoicing. ML algorithms are well-suited for IDP tasks due to their ability to learn from large amounts of data. For example, you could train an ML algorithm on a dataset of labeled documents to learn how to classify new documents. 

Also see: Custom AI Development vs. Off-the-Shelf AI Solutions

IDP vs. Optical Character Recognition (OCR)

How is IDP different from Optical Character Recognition (OCR)? At its simplest, IDP can be thought of as an extension of OCR. OCR focuses on extracting text from digital images, while IDP also includes the ability to interpret and understand the meaning of the text.

Therefore, you can use IDP to extract data from documents and process and make decisions based on that data. For example, IDP can automatically classify documents based on their content or extract specific information from a contract and populate a database. IDP is, therefore, a more sophisticated technology that uses artificial intelligence to identify and extract data from several document formats.

In addition, with IDP solutions, you can convert both unstructured and semi-structured data into structured, usable information. And with this, you can build end-to-end automation into business processes that are heavily reliant on unstructured and semi-structured data locked in documents. This ability to automate complex document-based workflows at the enterprise level is a huge advantage.

In fact, without IDP tools, Robotic Process Automation (RPA) processes would require a human in the loop to read documents and extract data to complete the process. Therefore, IDP is also the key to unlocking the true potential of RPA.

Data Types Encountered During Intelligent Document Processing

There are three main types of data encountered during IDP:

  • Visual Data: This is data conveyed through images or graphics and can include things like charts, diagrams, and photographs. IDP uses Computer Vision technology to understand the data.
  • Textual Data: Textual data is conveyed through words and includes emails, articles, and books. It is organized into linguistically relevant units such as characters, sentences, and paragraphs. IDP uses Natural Language Processing technology to process this data.
  • Numerical Data: This is data conveyed through numbers and can include things like financial data, social media data, and sensor data. It is often organized into a table or spreadsheet. IDP can use various techniques to understand this data, such as pattern recognition and statistical analysis.

Benefits of IDP

There are many benefits of using IDP, including:

Improved Accuracy

IDP is vital for preserving accuracy in document processing. By using IDP, businesses can take steps to prevent data entry errors that can often occur when employees are manually transcribing information from one document to another.

Instead of relying on humans to input data accurately, IDP uses artificial intelligence and machine learning to extract information from documents automatically. This reduces the potential for error and saves time and money by reducing the need for manual data entry. In addition, you can use IDP to verify the accuracy of data that has already been entered into a system, ensuring that everything is correct before moving forward.

Reduced Costs

The cost of processing documents can be high, particularly for businesses that deal with a large volume of documents daily. By automating document-based workflows, companies can reduce time and money spent on manual tasks such as data entry, sorting, and filing. In addition, IDP can help businesses to avoid late fees and other penalties by ensuring that documents are processed on time.

Process Efficiency

One of the most significant benefits of IDP is the increased efficiency it can bring to business processes. Businesses can free up employees to focus on more value-added tasks by automating document-based tasks. In addition, IDP can help to ensure that processes are completed in a timely and accurate manner, reducing the need for rework.

Improved Customer Service

In addition to improving efficiency, IDP can also help to improve customer service. Businesses can provide their customers with faster turnaround times and improved accuracy. IDP can also help companies keep track of customer correspondence and ensure that all customer inquiries are addressed promptly.

Compliance

In many industries, compliance is essential to protecting businesses and customers. Organizations can ensure that they are always compliant with the latest regulations. In addition, IDP can be used to monitor compliance-related correspondence and ensure that all required documents are filed on time.

Higher Straight-Through Processing (STP)

One of the ultimate goals of IDP is to achieve high rates of straight-through processing (STP). Documents are automatically processed without the need for manual intervention by knowledge workers. By achieving high rates of STP, businesses can improve efficiency, accuracy, and compliance while reducing costs.

Also see: How Improving Employee Experience Boosts Competitive Advantage 

Intelligent Document Processing Use Cases

IDP streamlines business processes in several ways. Some everyday use cases include:

Accounts Payable

IDP can be used to automate the accounts payable process by extracting data from invoices and other documents. This data can then be used to generate payments and reconcile accounts.

Accounts Receivable

IDP can also be used to streamline the accounts receivable process. To automate the billing and collections process, you can extract data from customer invoices and other documents.

AML (Anti-Money Laundering) Automation

Another common use case for Intelligent Document Process is anti-money laundering. US banks must comply with the Bank Secrecy Act and related regulations. Banks and other financial institutions use IDP to automate the process of identifying and reporting suspicious activity.

Insurance Claims Processing

Insurance companies often use IDP to streamline the claims processing workflow. Data is extracted from documents such as police reports, medical records, and insurance forms to automate the claims adjudication process.

Contract  Management

Businesses can use IDP to manage their contract portfolio. Data is extracted from contracts and other documents to track key dates, monitor compliance, and generate reports.

Customer Onboarding

Many businesses use IDP to automate the customer onboarding process. They do this by extracting data from customer application forms and other documents to open new accounts and initiate services.

Employee Onboarding

Similar to customer onboarding, Human Resources Departments use IDP to automate the employee onboarding process. Data is extracted from employment applications and other documents to initiate background checks, create employee files, and generate offer letters.

Loan Processing

Banks and other lending institutions use IDP to streamline the loan processing workflow. By extracting data from loan applications and other documents, they can automate the decision-making process.

Mortgage Underwriting

Mortgage underwriting is another common use case for IDP where data is extracted from mortgage applications and other documents in order to assess risk and make lending decisions.

Order Processing

Businesses use IDP to streamline the order processing workflow. Data is extracted from customer orders and other documents in order to generate pick tickets, invoices, and shipping labels.

Policy Administration

Insurance companies use IDP to automate the policy administration process. By extracting data from insurance applications and other documents, they can generate policy documents and bind coverage.

Supply Chain Management

Businesses use IDP to automate the supply chain management process. By extracting data from purchase orders and other documents, they are able to track inventory, manage supplier relationships, and generate reports.

Getting Started with IDP

A well-functioning enterprise relies on the efficient flow of information. From data analytics to document archiving, getting the right information to the right people at the right time is critical for success.

IDP can help you automate and streamline a wide variety of business processes, saving you time and money. It can also help you make better decisions by providing accurate and up-to-date information. If you want to leverage the unstructured data in your network, IDP is a great place to start.

Kihara Kimachia
Kihara Kimachia
Kihara Kimachia has been a writer and digital marketing consultant for more than ten years. He has a great passion for technology and currently works freelance for several leading tech websites.

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